Neural network algorithm for FADS system applied to the vehicles with sharp wedged fore-bodies

With respect to the difficulty in the modeling of the Flush Air Data Sensing System (FADS) for vehicles with sharp wedged fore-bodies and low solving precision of the model, applications of the artificial neural network algorithm for FADS system to sharp wedged fore-bodies were investigated in this paper. Back-propagation (BP) neural network model were set up to replace traditional aerodynamic model of the FADS system. Regarding the characteristics of the FADS system for vehicles with sharp wedged fore-bodies, neural network architecture with single hidden layer and double hidden layers were designed and performed on the basis of reasonable validation testing and structural parameters of network. The comparison was systematically analyzed between the testing error distributions of these two models. Flight parameters such as angle of attack, angle of sideslip, the free stream static pressure, and the Mach number were determined according to the network algorithm. Numerical simulation results show that the developed BP neural network algorithm has good accuracy for the vehicle with sharp wedged fore-bodies. Moreover, the accuracy of the neural network model with double hidden layers is higher than that of the network model with single hidden layer.